[Research on motion impedance cardiography de-noising method based on two-step spectral ensemble empirical mode decomposition and canonical correlation analysis].

Yao Xie, Dong Yang, Honglong Yu, Qilian Xie
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引用次数: 0

Abstract

Impedance cardiography (ICG) is essential in evaluating cardiac function in patients with cardiovascular diseases. Aiming at the problem that the measurement of ICG signal is easily disturbed by motion artifacts, this paper introduces a de-noising method based on two-step spectral ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA). Firstly, the first spectral EEMD-CCA was performed between ICG and motion signals, and electrocardiogram (ECG) and motion signals, respectively. The component with the strongest correlation coefficient was set to zero to suppress the main motion artifacts. Secondly, the obtained ECG and ICG signals were subjected to a second spectral EEMD-CCA for further denoising. Lastly, the ICG signal is reconstructed using these share components. The experiment was tested on 30 subjects, and the results showed that the quality of the ICG signal is greatly improved after using the proposed denoising method, which could support the subsequent diagnosis and analysis of cardiovascular diseases.

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[基于两步谱集合经验模式分解和典型相关分析的运动阻抗心动图去噪方法研究]。
阻抗心动图(ICG)对评估心血管疾病患者的心脏功能至关重要。针对 ICG 信号的测量容易受到运动伪影干扰的问题,本文介绍了一种基于两步频谱集合经验模式分解(EEMD)和卡农相关分析(CCA)的去噪方法。首先,分别在 ICG 和运动信号、心电图(ECG)和运动信号之间进行第一次频谱 EEMD-CCA。将相关系数最大的分量设为零,以抑制主要的运动伪影。其次,对获得的心电图和 ICG 信号进行第二次频谱 EEMD-CCA 以进一步去噪。最后,利用这些共享分量重建 ICG 信号。实验在 30 名受试者身上进行了测试,结果表明,在使用了所提出的去噪方法后,ICG 信号的质量得到了极大的改善,可以为后续的心血管疾病诊断和分析提供支持。
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来源期刊
生物医学工程学杂志
生物医学工程学杂志 Medicine-Medicine (all)
CiteScore
0.80
自引率
0.00%
发文量
4868
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